Generating time-based label refinements to discover more precise process models

Abstract

Process mining is a research field focused on the analysis of event data with the aim of extracting insights related to dynamic behavior. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights into (un)healthy habits and to contribute to ambient assisted living solutions. Finding the right event labels to enable the application of process mining techniques is however far from trivial, as simply using the triggering sensor as the event label for sensor events results in uninformative models that allow for too much behavior (i.e., the models are overgeneralizing). Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models. However, there exists no automated approach to generate refinements of event labels in the context of process mining. In this paper we propose a framework for the automated generation of label refinements based on the time attribute of events, allowing us to distinguish behaviorally different instances of the same event type based on their time attribute. We show on a case study with real-life smart home event data that using automatically generated refined event labels in process discovery, we can find more specific, and therefore more insightful, process models. We observe that one label refinement could affect the usefulness of other label refinements when used together. Therefore, the order in when label refinements are selected could be of relevance when selecting multiple label refinements. To investigate the size of this effect in practice, we evaluate four strategies that take interplay between label refinements into account in different degrees on three real-life smart home event logs. These label refinement selection strategies range from linear time complexity for the strategy that does not at all account for the interplay between label refinements to a factorial time complexity for the strategy that fully accounts for this interplay effect. We found that in practice there is no difference between the quality of the process models that were discovered with the four label refinement strategies. Therefore, the effect of interplay between label refinements seems limited in practice and simple and fast strategies can be used to select multiple label refinements.